This step applies a “learning-to-rank" [1]} technique on the training instances of the dataset. A pairwise supervised variant is used, since the order of two given technologies is not affected by other technologies in the list. For example, moment is more relevant than date-fns regardless of other alternative technologies. Among the various learning-to-rank algorithms reported in the literature [2]}, we chose GBRank [3]}
due to its effectiveness in Web search ranking [4]}, [5]}, [6]}. GBRank is based on a gradient boosting method for minimizing wrong preference predictions.
